Quantum computing represents a promising paradigm for solving complex problems, such as large-number factorization, exhaustive search, optimization, and mean and median computation. On the other hand, supervised learning deals with the classical induction problem where an unknown input-output relation is inferred from a set of data that consists of examples of this relation. Lately, because of the rapid growth of the size of datasets, the dimensionality of the input and output space, and the variety and structure of the data, conventional learning techniques have started to show their limits. Considering these problems, the purpose of this chapter is to illustrate how quantum computing can be useful for addressing the computational issues of building, tuning, and estimating the performance of a model learned from data.

Quantum computing and supervised machine learning: Training, model selection, and error estimation

Oneto, L.;Ridella, S.;Anguita, D.
2016

Abstract

Quantum computing represents a promising paradigm for solving complex problems, such as large-number factorization, exhaustive search, optimization, and mean and median computation. On the other hand, supervised learning deals with the classical induction problem where an unknown input-output relation is inferred from a set of data that consists of examples of this relation. Lately, because of the rapid growth of the size of datasets, the dimensionality of the input and output space, and the variety and structure of the data, conventional learning techniques have started to show their limits. Considering these problems, the purpose of this chapter is to illustrate how quantum computing can be useful for addressing the computational issues of building, tuning, and estimating the performance of a model learned from data.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/881457
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